Take Home Exercise 2

1. Overview

In this take-home exercise, a selected Tableau visualization submitted by classmate in take-home exercise 1 is reviewed based on its clarity and aesthetics.
Constructive suggestions are given based on data visualization design principles and best practices learned in Lesson 1 and 2. An alternative design is also proposed to help improve on the original visual. Instead of Tableau, the proposed design is produced using ggplot2, ggplot2 extensions and tidyverse packages.

a. Load the required libraries

results <- 'hide'
packages = c('tidyverse', 'plotly', 'ggthemes', "knitr", "rmarkdown")
for(p in packages){
  if(!require(p,character.only = T)){
    install.packages(p)
  }
  library(p,character.only = T)
}

b. Import the data

data2022 <- read_csv("respopagesextod2022.csv")

Rows: 100928 Columns: 7── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (5): Planning_Areas, SubZone, Age_Group, Sex, Type_of_Dwelling
dbl (2): Population, Time
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(data2022)

c. Group data by planning area, age group and gender

data2022$Age_Group <- factor (data2022$Age_Group, levels = unique(data2022$Age_Group)) 
data2022$Planning_Areas <- factor (data2022$Planning_Areas, levels = unique(data2022$Planning_Areas)) 
data_grp <- data2022 %>%
                    group_by(`Planning_Areas`, `Age_Group`, `Sex`) %>%
                    summarise('Population_Count'= sum(`Population`)) %>%
                    ungroup()

`summarise()` has grouped output by 'Planning_Areas', 'Age_Group'. You can override using the `.groups` argument.

paged_table(data_grp)

d. Select the top 9 planning areas by population count

top9PA <- data_grp %>%
  group_by(Planning_Areas) %>%
  summarise(count = sum(Population_Count)) %>%
  arrange(desc(count),.by_group = TRUE) %>%
  top_n(9,count)

data_top9 <- data_grp[data_grp$Planning_Areas %in% top9PA$Planning_Areas,]
print(unique(data_top9$Planning_Areas))

e. Converting population count of males to negative.

In order to plot age-sex pyramids, we need to convert the population count for one of the gender to negative. As we want “Male” to appear on the left side of the pyramids, as to mimic the original visual, we will convert male population count to negative using the mutate function.

data_males <- data_top9 %>%
  filter(`Sex` ==  "Males") %>%
  mutate (Population_Count = -Population_Count)

f. Combining the converted males population count to female population count.

data_females <- data_top9 %>%
  filter(`Sex` ==  "Females") 
data_transform <- rbind(data_males,data_females)

g. Plotting the age-sex pyramid.

xbrks <- seq(-15000, 15000, 5000)
xlabls <- paste0(as.character(c(seq(15, 0, -5), seq(5, 15, 5))),"k")
my_colors <- c("salmon","skyblue")
p <-ggplot(data_transform, aes (x = reorder(Age_Group, desc(Age_Group)), y = Population_Count, fill = Sex)) +
    geom_bar(stat = "identity", width = .7) +
    facet_wrap (~ Planning_Areas, nrow = 3, ncol = 3) + 
    scale_y_continuous(breaks = xbrks, labels = xlabls, name = "Population Count") +
    xlab("Age\nGroups") +
    coord_flip() + 
    theme_bw() + scale_fill_manual(values = my_colors) + 
    labs(title = "Age-sex pyramids of top 9 planning areas by population count in Singapore, June 2022") 
p1 <- p + theme(axis.text = element_text(size=16), 
          axis.title = element_text(size =21),
          axis.title.y= element_text(angle=0),
          axis.ticks.y = element_blank(),
          strip.text = element_text(size=22),
          plot.title = element_text(size=29, hjust = 0.5),
          legend.key.size = unit(2, 'cm'),
          legend.title = element_text(size=21),
          legend.text = element_text(size=16),
          legend.position = "right")  
    
show(p1)

h. Adding interactivity.

p2 <- p + theme(axis.text = element_text(size=5.5), 
          axis.title = element_text(size =9),
          axis.title.y= element_text(angle=0),
          axis.ticks.y = element_blank(),
          strip.text = element_text(size=8),
          plot.title = element_text(size=10, hjust = 0.5),
          legend.key.size = unit(1, 'cm'),
          legend.title = element_text(size=9),
          legend.text = element_text(size=6),
          legend.position = "right")  
ggplotly(p2)